Investigating the Impact of Covariate Inclusion on Sample Size Requirements of Factor Mixture Modeling: A Monte Carlo Simulation Study

2021 ◽  
pp. 1-9
Author(s):  
Yan Wang ◽  
Hsien-Yuan Hsu ◽  
Eunsook Kim
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Sample Size ◽  
Simulation Study ◽  
Mixture Modeling ◽  
Monte Carlo Simulation Study ◽  
Factor Mixture Modeling ◽  
The Impact

Uncertainty in Stock Composition Estimates of Oceanic Steelhead Trout Using Electrophoretic Characteristics: Comments on a Recent Study

1988 ◽  
Vol 45 (3) ◽  
pp. 432-442 ◽  
Author(s):  
T. J. Mulligan ◽  
S. McKinnell ◽  
C. C. Wood
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Sample Size ◽  
Simulation Study ◽  
Recent Literature ◽  
Steelhead Trout ◽  
Monte Carlo Simulation Study ◽  
Electrophoretic Data ◽  
Mixed Stock ◽  
Insight Into

Analysis of stock composition of mixed-stock fisheries using electrophoretic data is gaining acceptance for both research and management purposes. However, a thorough understanding of the influence of sample size, stock separation, and estimation procedures is required before meaningful results can be obtained. An example from the recent literature is reanalyzed to demonstrate this conclusion. We show how widely different results are obtained from the same data when analyzed by two different models. Some insight into these differences is achieved through a Monte Carlo simulation study.

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